PyCon DE & PyData 2026

With Large Language Models (LLMs), generating high-quality text and images is easy and so is
misusing it. As AI-generated content becomes harder to distinguish from human generated content,
developers are increasingly asking: How can we verify whether a piece of text comes from an LLM?
We’ll explore Python’s simplicity and rich ecosystem of libraries to solve this problem.

This talk introduces the foundations of LLM watermarking and shows how developers can implement
these techniques entirely in Python. We’ll discuss two core approaches, EXP sampling method and
KGW method. We will walk through the implementation of the KGW method using simple,
transparent code, and compare it with the EXP approach. There's no need for a large model or a GPU
cluster to understand how these systems work and the core ideas can be implemented in pure
Python using simple code. The code repositories, which includes both methods will be provided so
that the attendees can follow along.

Along the way, we’ll discuss the trade-offs and the limitations of current research. And for those
wondering, “Do I have to implement all this myself?”, the talk concludes with a demo of MarkLLM, an
existing open-source toolkit that provides a unified Python interface for experimenting with
watermarking algorithms.

Attendees will leave with a clear understanding of how watermarking works, when it’s useful, and
how to integrate these techniques into real-world Python projects.